MightyMothra

┐('~`; )┌ w/e

[oidial]

anything/anyone can be a sheep if you believe

shes a border collie!!! so she takes medicine to dampen her herding instinct!!! except when she doesn't.... ! all sheep* to the sheep corner NOW

*sheep/ sheep adjacent

and some backgrounds! some of you have not realized these aren't apart of the comic and thought i was doing some scary horror 'they weren't real' bit, so i have added a WARNING! <3

OK, TEEHEE, BYE!!!!!

[foxpost-generator]

The phrase "anti-herding meds" is in daily use, thank you.

[monixxdesign]

Back to the future-inspired bass guitar by Doner Designs (Source: 🎸 )

[roseillest]

FRITZ THE CAT (1972) dir. RALPH BAKSHI

[acquired-stardust]

.hack//Fragment Playstation 2 2005

[roseillest]

FRITZ THE CAT (1972) dir. RALPH BAKSHI

[akaname-chaosworld]

[my-dailydreams]

[cheaphalloweencostume]

sorry i really don’t like just reposting someone else’s joke but i haven’t been able to stop thinking about Yucky Stinking Tooth for weeks

[puppygirldick]

Jacek Yerka - Tsunami --

[soulspark]

[acerby]

[spaceshiprocket]

Elektra by Bill Sienkiewicz

[girls-in-2d]

tatiana wisla - last exile

[ink-the-artist]

The Neighbors Are Getting Drunk and Singing in the Wheat Fields Again

Keep reading

[justalurkr]

Text of tweet under the cut because it is loooong.

But... Stochastic Parrots.

Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.

Her name is Timnit Gebru.

She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.

The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.

The paper had not even been published yet.

Here is what she actually wrote, and why every prediction inside it has now come true.

The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.

This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.

The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.

The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.

Every one of those things has now been documented in deployment.

Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.

The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.

In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.

The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.

In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.

The fifth warning was the one Google cared about most.

Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.

This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.

The paper Google fired her for predicted the model collapse problem before model collapse had a name.

The mechanism behind why this all happened is the part of her work that nobody quotes.

Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.

She was making that argument from inside Google.

Then Google proved her right by removing her.

The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.

Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.

Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.

The question worth sitting with is the one almost no one in the industry will say out loud.

Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.

The researcher Google fired for warning about exactly what is now happening was right.

The company that fired her is now the second-largest deployer of the technology she warned about.

And the people inside that company who agree with her are not allowed to say so.

[c-rowlesblogs]

thinking about this in light of the news that google is giving its search engine a completely AI-powered facelift, obscuring the actual sources it pulls answers to user queries from, showing the user the singular, homogenous false face of Google AI rather than an array of clickable links to individual websites with individual people’s words and works on them. I’ve been seeing people say that this change is all about data harvesting, keeping closer track of what people search for and extracting more information about users as they engage with the AI chat… but in my opinion, this change is much more about control.

Google does not want to merely be the best portal to access information, which Google Search has been for twenty years; Google wants to be the source of that information. It does not want people to explore the internet as a world wide web of knowledge built by countless other humans, it doesn’t want users to connect to and learn from one another. It wants users to connect to Google, and stop there, and stay there, and slurp up whatever Google’s LLM serves up to them through a slot in the wall of their sterile enclosure.

Google and other AI companies want people’s critical thinking and independent research skills to atrophy. Curious people with the skills to satisfy that curiosity themselves, without asking gemini or claude or chat yipiti, are independent people, and independent people may independently learn things about AI companies, the politics of those companies’ leaders, and any number of related or seemingly-unrelated subjects, that are not flattering to or in line with what those companies would rather they think. Independent people do not tolerate a company building an enclosure around them.

But a user whose skills are atrophied, or who never developed them in the first place, is happy in the enclosure, because it is safe, and they can go to that slot in the wall and get their curiosity about the world satisfied with a plate of mush, and the mush is tasty and inoffensive and is dished up by their best friend and benevolent authority figure Google, and outside the enclosure is so scary and wild, and who would want to go out there, anyway?

[sommeranimation]

I’ve fallen in love with her. She’s everything to me.

[roboticjanitor]

Hard to dislike Vyper "I'm a work in progress" Deadlock

[jamwich]

btw 2026 this is the year star trek fictional baseball player buck bokai will make history.

excellent example of star trek naming two real people and a fake one except my friend and i didnt know enough about baseball to know he was fake when he was on ds9

[redstonedust]

welcome to my epic fantasy world. we have many developed fantasy kingdoms with 2000 years of rich lore and history. and to the south you can see the evil sand people and to the east theres japanchina.